Hospital Performance Measures Research Articles

An Approach Toward Public Hospital Performance Assessment

Background: Public hospitals have come under heavy scrutiny across the world owing to rising expenditures. However, much of the focus has been on cutting down costs to raise efficiency levels. Although not denying the importance of efficiency measures, this article targets a performance issue that is relevant to address the quality of services rendered in public hospitals. Thus, it is important to focus on the effectiveness of resource utilization in these hospitals. Consequently, this article seeks to examine the impact of average length of stay (ALOS) and bed turnover rates (BTR) on bed occupancy rates (BOR). Methods: Public hospital inpatient utilization records during the period 2006 to 2013 were gathered from the Ministry of Health, Malaysia. A 2-step generalized method of moments (GMM) statistical method was used to analyze the data. BOR was adopted as the dependent variable, whereas BTR and ALOS were used as the explanatory variables. The logarithm of total bed count (BED), admission (ADM), and patient days (PD) was deployed as control variables. Three regression models were developed to explore the correlates of BOR as a hospital performance measure. Ethics committee approval was waived because no patients were identified in the study. Results: The statistical analyses show that ALOS and BTR are inversely correlated with BOR, with both coefficients significant at 1%. The control variables of BED, ADM, and PD had the right positive signs and they were significant in both sets of equations. Hence, reducing ALOS and BTR can help raise performance of public hospitals in Malaysia. Conclusion: In light of the robust results obtained, this study offers implications for improving public hospital performance. It shows a need to reduce ALOS and BTR in public hospitals to improve BOR.

Impact on hospital ranking of basing readmission measures on a composite endpoint of death or readmission versus readmissions alone

BackgroundReadmission penalties are central to the Centers for Medicare and Medicaid Services (CMS) efforts to improve patient outcomes and reduce health care spending. However, many clinicians believe that readmission metrics may unfairly penalize low-mortality hospitals because mortality and readmission are competing risks. The objective of this study is to compare hospital ranking based on a composite outcome of death or readmission versus readmission alone.MethodsWe performed a retrospective observational study of 344,565 admissions for acute myocardial infarction (AMI), congestive heart failure (CHF), or pneumoniae (PNEU) using population-based data from the New York State Inpatient Database (NY SID) between 2011 and 2013. Hierarchical logistic regression modeling was used to estimate separate risk-adjustment models for the (1) composite outcome (in-hospital death or readmission within 7-days), and (2) 7-day readmission. Hospital rankings based on the composite measure and the readmission measure were compared using the intraclass correlation coefficient and kappa analysis.ResultsUsing data from all AMI, CHF, and PNEU admissions, there was substantial agreement between hospital adjusted odds ratio (AOR) based on the composite outcome versus the readmission outcome (intraclass correlation coefficient [ICC] 0.67; 95% CI: 0.56, 0.75). For patients admitted with AMI, there was moderate agreement (ICC 0.53; 95% CI: 0.41, 0.62); for CHF, substantial agreement (ICC 0.72; 95% CI: 0.66, 0.78); and for PNEU, substantial agreement (ICC 0.71; 95% CI: 0.61, 0.78). There was moderate agreement when the composite and readmission metrics were used to classify hospitals as high, average, and low-performance hospitals (κ = 0.54, SE = 0.050). For patients admitted with AMI, there was slight agreement (κ = 0.14, SE = 0.037) between the two metrics.ConclusionsHospital performance on readmissions is significantly different from hospital performance on a composite metric based on readmissions and mortality. CMS and policy makers should consider re-assessing the use of readmission metrics for measuring hospital performance.

Differences in Hospital Risk-standardized Mortality Rates for Acute Myocardial Infarction When Assessed Using Transferred and Nontransferred Patients.

One in 5 patients with acute myocardial infarction (AMI) are transferred between hospitals. However, current hospital performance measures based on AMI mortality exclude these patients from the evaluation of referral hospitals. To determine the relationship between risk-standardized mortality for transferred and nontransferred patients at referral hospitals. This is a retrospective cohort study. Fee-for-service Medicare claims from 2011 for patients hospitalized with a primary diagnosis of AMI, at hospitals admitting at least 15 patients in transfer. Hospital-specific risk-standardized 30-day mortality rates (RSMRs) for 2 groups of patients: those admitted through transfer from another hospital, and those natively admitted without a preceding or subsequent interhospital transfer. There were 304 hospitals admitting at least 15 patients in transfer. These hospitals cared for 77,711 natively admitted patients (median, 254; interquartile range, 162-321), and 11,829 patients admitted in transfer (median, 26; interquartile range, 19-46). Risk-standardized mortality rates were higher for natively admitted patients than for those admitted in transfer (mean, 11.5%±1.2% vs. 7.2%±1.1%). There was weak correlation between hospital performance as assessed by RSMR for patients natively admitted versus those admitted in transfer (Pearson r=0.24, P<0.001); when performance was arrayed by quartile, 102 hospitals (33.6%) differed at least 2 quartiles of performance across the 2 patient groups. For Medicare patients with AMI, hospital-specific RSMRs for natively admitted patients are only weakly associated with RSMRs for patients transferred in from another hospital. Current AMI performance metrics may fail to provide guidance about hospital quality for transferred patients.

A case study of healthcare providers’ goals during interprofessional rounds

ABSTRACTDaily interprofessional rounds enhance collaboration among healthcare providers and improve hospital performance measures. However, it is unclear how healthcare providers’ goals influence the processes and outcomes of interprofessional rounds. The purpose of this case study was to explore the goals of healthcare providers attending interprofessional rounds in an internal medicine ward. The second purpose was to explore the challenges encountered by healthcare providers while pursuing these goals. Three focus groups were held with healthcare providers of diverse professional backgrounds. Focus group field notes and transcripts were analysed using thematic analysis. The data indicated that there was no consensus among healthcare providers regarding the goals of interprofessional rounds. Discharge planning and patient care delivery were perceived as competing priorities during rounds, which limited the participation of healthcare providers. Nevertheless, study participants identified goals of rounds that were relevant to most care providers: developing shared perspectives of patients through direct communication, promoting collaborative decision making, coordinating care, and strengthening interprofessional relationships. Challenges in achieving the goals of interprofessional rounds included inconsistent attendance, exchange of irrelevant information, variable participation by healthcare providers, and inconsistent leadership. The findings of this study underscore the importance of shared goals in the context of interprofessional rounding.

Variation in readmission and mortality following hospitalisation with a diagnosis of heart failure: prospective cohort study using linked data

BackgroundHospitalisation for heart failure is common and post-discharge outcomes, including readmission and mortality, are often poor and are poorly understood. The purpose of this study was to examine patient- and hospital-level variation in the risk of 30-day unplanned readmission and mortality following discharge from hospital with a diagnosis of heart failure.MethodsProspective cohort study using data from the Sax Institute’s 45 and Up Study, linking baseline survey (Jan 2006-April 2009) to hospital and mortality data (to Dec 2011). Primary outcomes in those admitted to hospital with heart failure included unplanned readmission, mortality and combined unplanned readmission/mortality, within 30 days of discharge. Multilevel models quantified the variation in outcomes between hospitals and examined associations with patient- and hospital-level characteristics.ResultsThere were 5074 participants with a heart failure admission discharged from 251 hospitals; 1052 (21%) had unplanned readmissions, 186 (3.7%) died, and 1146 (23%) had either/both outcomes within 30 days of discharge. Crude outcomes varied across hospitals, but between-hospital variation explained little of the total variation in outcomes (intraclass correlation coefficients (ICC) after inclusion of patient factors: 30-day unplanned readmission ICC = 0.0125 (p = 0.24); death ICC = 0.0000 (p > 0.99); unplanned readmission/death ICC = 0.0266 (p = 0.07)). Patient characteristics associated with a higher risk of unplanned readmission included: being male (male vs female, adjusted odds ratio (aOR) = 1.18, 95% CI: 1.00–1.37); prior hospitalisation for cardiovascular disease (aOR = 1.44, 1.08–1.91) and for anemia (aOR = 1.36, 1.14–1.63); comorbidities at admission (severe vs none: aOR = 1.26, 1.03–1.54); lower body-mass-index (obese vs normal weight: aOR = 0.77, 0.63–0.94); and lower social interaction scores. Similarly, risk of 30-day mortality was associated with patient- rather than hospital-level factors, in particular age (≥85y vs 45–< 75y: aOR = 3.23, 1.93–5.41) and comorbidity (severe vs none: aOR = 2.68, 1.82–3.94).ConclusionsThe issue of high readmission and mortality rates in people with heart failure appear to be system-wide, with the variation in these outcomes essentially attributable to variation between patients rather than hospitals. The findings suggest that there are limitations in using these outcomes as hospital performance measures in this patient population and support the need for patient-centred strategies to optimise heart failure management and outcomes.

Abstract 167: Correlation of 30-day Hospital Readmission Rates by Payer and Penalty Status

Background: Readmission rates for heart failure (HF), acute myocardial infarction (MI), and pneumonia (PNA) hospitalizations among Medicare beneficiaries are used to assess hospital quality and determine global reimbursement. However, whether these measures reflect readmission rates for other conditions or insurance groups is unknown. Methods: All hospitals with &gt;30 index admissions for HF, MI or pneumonia in 2013 in the all-payer Nationwide Readmissions Database (NRD) were included. For each hospital, we estimated 30-day all-cause risk-standardized readmission rates (RSRRs) for 3 groups: (A) Medicare beneficiaries admitted with HF/MI/PNA, (B) Medicare beneficiaries admitted for other conditions, and (C) non-Medicare beneficiaries admitted for HF/MI/PNA. Pair-wise correlations of these measures were assessed using Spearman correlation coefficients. Hospitals were divided into quartiles based on their calculated RSRR for each group, and inter-rater agreement between groups was assessed using weighted kappa statistics. We then examined pair-wise Spearman correlations among subgroups based on hospital characteristics (size, ownership status, and teaching status). Results: Among 1,228 hospitals, wide variation in RSRRs was seen across conditions and insurance type. Groups A vs. B had moderate correlation and agreement (Spearman 0.631, quartile weighted kappa 0.440). However, A vs. C had only modest correlation and fair agreement (Spearman 0.399, quartile weighted kappa 0.243). Compared with their quartile rank for the publicly reported group A, 46% of hospitals were ranked in the same quartile for group B and 35% for group C (Figure). Correlations between groups A vs. B and A vs. C were strongest among metro teaching hospitals (Spearman 0.674 and 0.507, respectively). Conclusions: Risk-standardized readmission rates for HF/MI/PNA are generalizable measures of a hospital’s readmission rate for other conditions among Medicare-insured patients. However, publicly reported RSRRs for HF/MI/PNA among Medicare patients are less suitable measures of hospital performance for non-Medicare patients admitted for these conditions.

Educational level and 30-day outcomes after hospitalization for acute myocardial infarction in Italy

BackgroundThere is a growing interest in the factors that influence short-term mortality and readmission after hospitalization for acute myocardial infarction (AMI) since such outcomes are commonly considered as hospital performance measures. Socioeconomic status (SES) is one of the factors contributing to healthcare outcomes after hospitalization for AMI. However, no study has been published on education and 30-day readmission in Europe. The objective of this study is to examine the association between educational level and 30-day mortality and readmission among patients hospitalized for AMI in Tuscany (Italy).MethodsA retrospective cohort study using data from hospital discharge records was conducted. The analysis included all patients discharged with a principal diagnosis of AMI between January 1, 2011, and November 30, 2014, from all hospitals in Tuscany. Educational level was categorized as low (no middle school diploma), mid (middle school diploma) and high (high school diploma or more). Three multilevel models were developed, sequentially controlling for patient-level socio-demographic and clinical variables and hospital-level variables. Patients were stratified by age (≤75 and >75 years).ResultsMortality analysis included 23,402 patients, readmission analysis included 22,181 patients. In both unadjusted and full-adjusted models, patients with a high education had lower odds of 30-day mortality compared to those patients with low education (OR age ≤ 75 years 0.67, 95% CI:0.47–0.94; OR age > 75 years 0.72, 95% CI:0.54–0.95). With regard to 30-day readmission, only patients aged over 75 years with a high education had lower odds of short-term readmission compared to those patients with low education (OR age > 75 0.73, 95% CI:0.58–0.93).ConclusionsAmong patients hospitalized in Tuscany for AMI, low levels of education were associated with increased odds of 30-day mortality for both age groups and increased odds of 30-day readmission only for patients aged over 75 years. Our findings suggest that the educational component should not be underestimated in order to improve short-term outcomes, which are considered as performance measures at the hospital level. Hospital managers might consider strategies that are sensitive to patients with low SES, such as providing post-hospitalization support to less-educated patients and promoting a healthier lifestyle, to improve both health equity and performance outcomes.

Does adding clinical data to administrative data improve agreement among hospital quality measures?

BackgroundHospital performance measures based on patient mortality and readmission have indicated modest rates of agreement. We examined if combining clinical data on laboratory tests and vital signs with administrative data leads to improved agreement with each other, and with other measures of hospital performance in the nation's largest integrated health care system. MethodsWe used patient-level administrative and clinical data, and hospital-level data on quality indicators, for 2007–2010 from the Veterans Health Administration (VA). For patients admitted for acute myocardial infarction (AMI), heart failure (HF) and pneumonia we examined changes in hospital performance on 30-d mortality and 30-d readmission rates as a result of adding clinical data to administrative data. We evaluated whether this enhancement yielded improved measures of hospital quality, based on concordance with other hospital quality indicators. ResultsFor 30-d mortality, data enhancement improved model performance, and significantly changed hospital performance profiles; for 30-d readmission, the impact was modest. Concordance between enhanced measures of both outcomes, and with other hospital quality measures – including Joint Commission process measures, VA Surgical Quality Improvement Program (VASQIP) mortality and morbidity, and case volume – remained poor. ConclusionsAdding laboratory tests and vital signs to measure hospital performance on mortality and readmission did not improve the poor rates of agreement across hospital quality indicators in the VA. InterpretationEfforts to improve risk adjustment models should continue; however, evidence of validation should precede their use as reliable measures of quality.

Inattentional blindness and failures to rescue the deteriorating patient in critical care, emergency and perioperative settings: Four case scenarios

BackgroundFailure to identify and respond to clinical deterioration is an important measure of patient safety, hospital performance and quality of care. Although studies have identified the role of patient, system and human factors in failure to rescue events, the role of ‘inattentional blindness’ as a possible contributing factor has been overlooked. ObjectivesTo explore the nature and possible patient safety implications of inattentional blindness in critical care, emergency and perioperative nursing contexts. MethodsAnalysis of four case scenarios drawn from a naturalistic inquiry investigating how nurses identify and manage gaps (discontinuities) in care. Data were collected via in-depth interviews from a purposeful sample of 71 nurses, of which 20 were critical care nurses, 19 were emergency nurses and 16 were perioperative nurses. Case scenarios were identified, selected and analysed using inattentional blindness as an interpretive frame. ResultsThe four case scenarios presented here suggest that failures to recognise and act upon patient observations suggestive of clinical deterioration could be explained by inattentional blindness. In all but one of the cases reported, vital signs were measured and recorded on a regular basis. However, teams of nurses and doctors failed to ‘see’ the early signs of clinical deterioration. The high-stress, high-complexity nature of the clinical settings in which these cases occurred coupled with high cognitive workload, noise and frequent interruptions create the conditions for inattentional blindness. ConclusionsThe case scenarios considered in this report raise the possibility that inattentional blindness is a salient but overlooked human factor in failure to rescue events across the critical care spectrum. Further comparative cross-disciplinary research is warranted to enable a better understanding of the nature and possible patient safety implications of inattentional blindness in critical care nursing contexts.

Mortalité à 30 jours après fracture de la hanche ou du bassin. Facteurs prédictifs

IntroductionWith the cost of healthcare in the United States reaching $2.9 trillion in 2013 and expected to increase with a growing geriatric population, the Center for Medicare and Medicaid Services (CMS) and Hospital Quality Alliance (HQA) began publicly reporting 30-day mortality rates so that hospitals and physicians may begin to confront clinical problems and promote high-quality and patient–centered care. Though the 30-day mortality is considered a highly effective tool in measuring hospital performance, little data actually exists that explores the rate and risk factors for trauma-related hip and pelvis fractures. Therefore, in this study, we sought to explore the risk factors associated with 30-day mortality in trauma-related hip and pelvic fractures. Materials and methodsUtilizing the ACS-NSQIP (American College of Surgeons National Surgical Quality Improvement Program) database, 341,062 patients undergoing orthopaedic procedures from 2005 to 2013 were identified through a Current Procedural Terminology (CPT) code search. A second CPT code search identified 24,805 patients who sustained a hip/pelvis fracture. Patient demographics, preoperative comorbidities, operative characteristics and postoperative complications were collected and compared using χ2 test, Wilcoxon-Mann-Whitney test and multivariate logistic regression analysis. ResultsPreoperative and postoperative risk factors for 30-day mortality following a hip/pelvis fracture were found: ASA classification, ascites, disseminated cancer, dyspnea, functional status, history of congestive heart failure (CHF), history of chronic obstructive pulmonary disease (COPD), a recent blood transfusion, and the postoperative complications: pneumonia, myocardial infarction, stroke, and septic shock. DiscussionSeveral preoperative patient risk factors and postoperative complications greatly increased the odds for patient mortality following 30-days after initial surgery. Orthopaedic surgeons can utilize these predictive risk factors to better improve patient care. Level of evidenceRetrospective study. Level IV.

Incidence and Etiology of Potentially Preventable ICU Readmissions.

The rate of unplanned ICU readmissions is often considered a measure of hospital performance. However, the degree to which these readmissions are preventable and the causes of preventable readmissions are unknown, creating uncertainty about the feasibility and value of reducing ICU readmission rates. To inform this issue, we sought to determine the frequency and underlying causes of potentially preventable ICU readmissions. Retrospective cohort study. Urban, academic medical center in the mid-Atlantic United States. Adult patients discharged alive from their first ICU admission with an unplanned readmission within 48 hours of discharge. Each patient's medical chart was reviewed by two independent investigators who rated each readmission's preventability according to standardized scale and assessed the etiology of both preventable and nonpreventable readmissions. We assessed concordance between raters using the κ statistic and resolved disagreements through iterative discussion. Of 136 readmissions in the final analysis, 16 (11.8%; 95% CI, 6.9-18.4) were considered preventable and 120 (88.2%; 95% CI, 81.5-93.1) were considered nonpreventable. Of nonpreventable readmissions, 67 were due to a new clinical problem and 53 were due to an existing clinical problem. Among preventable readmissions, six were attributable to system errors, six were attributable to management errors, two were attributable to procedural events, one was attributable to a diagnostic error, and one was attributable to a medication error. Compared to nonpreventable readmissions, preventable readmissions tended to have shorter index ICU lengths of stay (2 vs 3 d; p = 0.05) and a shorter duration of time on the ward prior to readmission (16.6 vs 23.6 hr; p = 0.05). The majority of early ICU readmissions are nonpreventable, raising important concerns about ICU readmission rates as a measure of hospital performance.

An approach toward public hospital performance assessment.

Background:Public hospitals have come under heavy scrutiny across the world owing to rising expenditures. However, much of the focus has been on cutting down costs to raise efficiency levels. Although not denying the importance of efficiency measures, this article targets a performance issue that is relevant to address the quality of services rendered in public hospitals. Thus, it is important to focus on the effectiveness of resource utilization in these hospitals. Consequently, this article seeks to examine the impact of average length of stay (ALOS) and bed turnover rates (BTR) on bed occupancy rates (BOR).Methods:Public hospital inpatient utilization records during the period 2006 to 2013 were gathered from the Ministry of Health, Malaysia. A 2-step generalized method of moments (GMM) statistical method was used to analyze the data. BOR was adopted as the dependent variable, whereas BTR and ALOS were used as the explanatory variables. The logarithm of total bed count (BED), admission (ADM), and patient days (PD) was deployed as control variables. Three regression models were developed to explore the correlates of BOR as a hospital performance measure. Ethics committee approval was waived because no patients were identified in the study.Results:The statistical analyses show that ALOS and BTR are inversely correlated with BOR, with both coefficients significant at 1%. The control variables of BED, ADM, and PD had the right positive signs and they were significant in both sets of equations. Hence, reducing ALOS and BTR can help raise performance of public hospitals in Malaysia.Conclusion:In light of the robust results obtained, this study offers implications for improving public hospital performance. It shows a need to reduce ALOS and BTR in public hospitals to improve BOR.

All-Payer Analysis of Heart Failure Hospitalization 30-Day Readmission: Comorbidities Matter

BackgroundThirty-day readmission following heart failure hospitalization impacts hospital performance measures and reimbursement. We investigated readmission characteristics and the magnitude of 30-day hospital readmissions after hospital discharge for heart failure using the Healthcare Cost and Utilization Project State Inpatient Databases (SID). MethodsAdults aged ≥ 40 years hospitalized with a primary discharge diagnosis of heart failure from 2007-2011 were identified in the California, New York, and Florida SIDs. Characteristics of patients with and without 7-, 8 to 30-, and 30-day readmission, and primary readmission diagnoses and risk factors for readmission were examined. ResultsWe identified 547,068 patients with mean age 74.7 years; 50.7% were female, and 65.4% were White. Of 117,123 patients (21.4%) readmitted within 30 days (median 12 days), 69.7% had a non-heart failure primary readmission diagnosis. Patients with 30-day readmissions more frequently had a history of previous admission with heart failure as a secondary diagnosis, fluid and electrolyte disorders, and chronic deficiency anemia. There were no significant clinical differences at baseline between those patients whose first readmission was in the first 7 days after discharge vs in the next 23 days. The most common primary diagnoses for 30-day non-heart failure readmissions were other cardiovascular conditions (14.9%), pulmonary disease (8.5%), and infections (7.7%). ConclusionsIn this large all-payer cohort, ∼70% of 30-day readmissions were for non-heart failure causes, and the median time to readmission was 12 days. Future interventions to reduce readmissions should focus on common comorbid conditions that contribute to readmission burden.

Using clinical data to predict high-cost performance coding issues associated with pressure ulcers: a multilevel cohort model.

Hospital-acquired pressure ulcers (HAPUs) have a mortality rate of 11.6%, are costly to treat, and result in Medicare reimbursement penalties. Medicare codes HAPUs according to Agency for Healthcare Research and Quality Patient-Safety Indicator 3 (PSI-03), but they are sometimes inappropriately coded. The objective is to use electronic health records to predict pressure ulcers and to identify coding issues leading to penalties. We evaluated all hospitalized patient electronic medical records at an academic medical center data repository between 2011 and 2014. These data contained patient encounter level demographic variables, diagnoses, prescription drugs, and provider orders. HAPUs were defined by PSI-03: stages III, IV, or unstageable pressure ulcers not present on admission as a secondary diagnosis, excluding cases of paralysis. Random forests reduced data dimensionality. Multilevel logistic regression of patient encounters evaluated associations between covariates and HAPU incidence. The approach produced a sample population of 21 153 patients with 1549 PSI-03 cases. The greatest odds ratio (OR) of HAPU incidence was among patients diagnosed with spinal cord injury (ICD-9 907.2: OR = 14.3; P < .001), and 71% of spinal cord injuries were not properly coded for paralysis, leading to a PSI-03 flag. Other high ORs included bed confinement (ICD-9 V49.84: OR = 3.1, P < .001) and provider-ordered pre-albumin lab (OR = 2.5, P < .001). This analysis identifies spinal cord injuries as high risk for HAPUs and as being often inappropriately coded without paralysis, leading to PSI-03 flags. The resulting statistical model can be tested to predict HAPUs during hospitalization. Inappropriate coding of conditions leads to poor hospital performance measures and Medicare reimbursement penalties.

Impact of Race/Ethnicity and Socioeconomic Status on Risk-Adjusted Hospital Readmission Rates Following Hip and Knee Arthroplasty.

Readmission rates following total hip arthroplasty (THA) and total knee arthroplasty (TKA) are increasingly used to measure hospital performance. Readmission rates that are not adjusted for race/ethnicity and socioeconomic status, patient risk factors beyond a hospital's control, may not accurately reflect a hospital's performance. In this study, we examined the extent to which risk-adjusting for race/ethnicity and socioeconomic status affected hospital performance in terms of readmission rates following THA and TKA. We calculated 2 sets of risk-adjusted readmission rates by (1) using the Centers for Medicare & Medicaid Services standard risk-adjustment algorithm that incorporates patient age, sex, comorbidities, and hospital effects and (2) adding race/ethnicity and socioeconomic status to the model. Using data from the Healthcare Cost and Utilization Project, 2011 State Inpatient Databases, we compared the relative performances of 1,194 hospitals across the 2 methods. Addition of race/ethnicity and socioeconomic status to the risk-adjustment algorithm resulted in (1) little or no change in the risk-adjusted readmission rates at nearly all hospitals; (2) no change in the designation of the readmission rate as better, worse, or not different from the population mean at >99% of the hospitals; and (3) no change in the excess readmission ratio at >97% of the hospitals. Inclusion of race/ethnicity and socioeconomic status in the risk-adjustment algorithm led to a relative-performance change in readmission rates following THA and TKA at <3% of the hospitals. We believe that policymakers and payers should consider this result when deciding whether to include race/ethnicity and socioeconomic status in risk-adjusted THA and TKA readmission rates used for hospital accountability, payment, and public reporting. Prognostic Level III. See instructions for Authors for a complete description of levels of evidence.

Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicator for Postoperative Respiratory Failure (PSI 11) does not identify accurately patients who received unsafe care

The Agency for Healthcare Research and Quality Patient Safety Indicator 11 is used to identify postoperative respiratory failure events and detect areas for quality improvement. This study examines the accuracy of Patient Safety Indicator 11 in identifying clinically valid patient safety events. All cases flagged for Patient Safety Indicator 11 from July 2013 to July 2015 by Agency for Healthcare Research and Quality QI Version 4.5 including International Classification of Diseases-9 codes were evaluated. Code-confirmed cases underwent independent review by 2 physicians. Inpatient electronic medical records were used to identify clinical factors for postoperative respiratory failure in each case to determine if postoperative respiratory failure was a result of unsafe care. The clinical true-positive rate and positive predictive value were calculated. A total of 166 postoperative respiratory failure cases were reviewed; 51 were recoded and reversed due to coding or documentation errors; 115 cases met the Agency for Healthcare Research and Quality definition of postoperative respiratory failure. A total of 71 (61.7%) of the 115 cases were false positives and did not reflect unsafe care, while 44 cases were true positives with a positive predictive value of 38.3%. χ(2) analysis did not reveal an association between demographics, clinical characteristics, or operative procedure with true-positive cases. Administrative coding data for Agency for Healthcare Research and Quality Patient Safety Indicator 11 do not identify accurately patients who received unsafe care when taking intoaccount unpreventable clinical factors causing postoperative respiratory failure. The use of Agency for Healthcare Research and Quality Patient Safety Indicator 11 as a hospital performance measure should be reconsidered until inclusion and exclusion criteria are revised.

Predictors of 30-day mortality following hip/pelvis fractures

IntroductionWith the cost of healthcare in the United States reaching $2.9 trillion in 2013 and expected to increase with a growing geriatric population, the Center for Medicare and Medicaid Services (CMS) and Hospital Quality Alliance (HQA) began publicly reporting 30-day mortality rates so that hospitals and physicians may begin to confront clinical problems and promote high-quality and patient-centered care. Though the 30-day mortality is considered a highly effective tool in measuring hospital performance, little data actually exists that explores the rate and risk factors for trauma-related hip and pelvis fractures. Therefore, in this study, we sought to explore the risk factors associated with 30-day mortality in trauma-related hip and pelvic fractures. Materials and methodsUtilizing the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database, 341,062 patients undergoing orthopaedic procedures from 2005 to 2013 were identified through a Current Procedural Terminology (CPT) code search. A second CPT code search identified 24,805 patients who sustained a hip/pelvis fracture. Patient demographics, preoperative comorbidities, operative characteristics and postoperative complications were collected and compared using Chi-squared test, Wilcoxon-Mann-Whitney test and multivariate logistic regression analysis. ResultsPreoperative and postoperative risk factors for 30-day mortality following a hip/pelvis fracture were found: ASA classification, ascites, disseminated cancer, dyspnea, functional status, history of congestive heart failure (CHF), history of chronic obstructive pulmonary disease (COPD), a recent blood transfusion, and the postoperative complications: pneumonia, myocardial infarction, stroke, and septic shock. DiscussionSeveral preoperative patient risk factors and postoperative complications greatly increased the odds for patient mortality following 30-days after initial surgery. Orthopaedic surgeons can utilize these predictive risk factors to better improve patient care. Level of evidenceRetrospective study. Level IV.

OECD 병원 성과 프로젝트의 동향과 국내 시사점

The Organization for Economic Cooperation and Development, which has continuously evaluated the performance of healthcare systems, has recently invested much effort into hospital performance measurement. The purpose of this paper is to introduce the hospital performance measurement programs operated by international organizations or at the national level based on the OECD's hospital performance project. Health Insurance Review & Assessment service (HIRA)'s quality assessment was analyzed based on the analytical framework of the OECD's hospital performance project. The hospital performance measurement programs of WHO, Canada, Australia, United States and United Kingdom are briefly explored, in view of the conceptual framework, key performance dimensions and indicators that are currently in use. The OECD suggested seven key dimensions of hospital performance: timeliness, efficiency, continuity, effectiveness and appropriateness, staff orientation, patient orientation and safety. The analysis of the quality assessment program of HIRA, which operates 36 diseases and procedures and 347 indicators, shows that the numbers of indicators are relatively small in the areas of safety, patient centeredness and efficiency. Continuity of care and staff orientation are not fully developed also, but the situations are similar in other countries. In conclusion, hospital performance measurement using stable and comprehensive data should be developed to improve overall system performance, and discussions on a conceptual framework that can lay out directions and key performance domains need to take into place.

Measuring and Forecasting Quality in English Hospitals

Summary Hospital performance metrics, often in the form of risk-adjusted hospital mortality rates, are increasingly being made available in the public domain to compare hospitals. Despite the proliferation of these metrics, uncertainty remains regarding their validity and reliability given the noise surrounding their underlying measures. The paper considers a quality measure of hospital performance developed by McClellan and Staiger which smooths within hospitals and over time, while remaining computationally straightforward. The McClellan and Staiger method improves on others by incorporating different measures of outcome, eliminating systematic bias arising from the heterogeneous mix of hospital outputs and the noise that is inherent in other measures of quality. The technique also allows the forecasting of future quality. Using English hospital episode statistics for the years 2000–2005 for acute myocardial infarction and hip replacement, we use this technique to return quality measures based on hospital fixed effects estimated from yearly cross-sectional patient level data, and vector auto-regressions estimated over time, which then combine information from different time periods and across conditions to produce robust hospital quality measures. Our results suggest that this method is well suited to measure and predict provider quality of care in the English setting.

The contribution of the composite of clinical process indicators as a measure of hospital performance in the management of acute coronary syndromes-insights from the CONCORDANCE registry.

Acute coronary syndrome (ACS) is a costly condition for health service provision yet variation in the delivery of care between hospitals persists. A composite measure of adherence with evidence-based clinical-process indicators (CPIs) could better inform hospital performance reporting and clinical outcomes in the management of ACS. Data on 7444 ACS patients from 39 Australian hospitals were used to derive a hospital-specific composite quality score by calculating mean adherence to 14 evidence-based CPIs. Using the generalized estimating equation to account for clustering of patients within hospitals and the GRACE risk score to adjust for differences in presenting risk, we evaluated associations between the hospital-specific composite quality score, in-hospital major adverse events, in-hospital mortality and mortality and readmission for ACS at 6 months. Hospitals had a mean adherence of 68.3% (SD 21.7) with the composite quality score. There was significant variation between hospital adherence tertile 1 (79%) and tertile 3 (56%), P < 0.0001. With risk adjustment, there was an association between hospitals with a higher composite quality score and reduced in-hospital adverse events (OR: 0.85, CI: 0.71-0.99) and survival at hospital discharge (OR: 0.47; 95% CI: 0.28-0.77). There was trending improvement in survival at 6 months (OR 0.48; CI: 0.20-1.16) and fewer readmissions to hospital for ACS at 6 months (OR 0.79; CI 0.60-1.05). The association between the quality composite score and reduced in-hospital events and survival at hospital discharge supports the utility of reporting CPIs in routine hospital performance reporting on the management of ACS. CONCORDANCE Registry ACTRN12614000887673.